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Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies

Published inJournal of Chemical Theory and Computation, vol. 16, no. 7, p. 4641-4654
Publication date2020
Abstract

Calculating absolute binding free energies is challenging and important. In this paper, we test some recently developed metadynamics-based methods and develop a new combination with a Hamiltonian replica-exchange approach. The methods were tested on 18 chemically diverse ligands with a wide range of different binding affinities to a complex target; namely, human soluble epoxide hydrolase. The results suggest that metadynamics with a funnel-shaped restraint can be used to calculate, in a computationally affordable and relatively accurate way, the absolute binding free energy for small fragments. When used in combination with an optimal pathlike variable obtained using machine learning or with the Hamiltonian replica-exchange algorithm SWISH, this method can achieve reasonably accurate results for increasingly complex ligands, with a good balance of computational cost and speed. An additional benefit of using the combination of metadynamics and SWISH is that it also provides useful information about the role of water in the binding mechanism.

Keywords
  • Algorithms
  • Drug Design
  • Epoxide Hydrolases/chemistry/metabolism
  • Humans
  • Ligands
  • Machine Learning
  • Molecular Dynamics Simulation
  • Protein Binding
  • Protein Structure, Tertiary
  • Thermodynamics
Funding
  • Autre - EPSRC (EP/M013898/1; EP/P022138/1; EP/P011306/1)
  • Autre - Swiss National Supercomputing Centre (CSCS) - s847
Citation (ISO format)
EVANS, Rhys et al. Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies. In: Journal of Chemical Theory and Computation, 2020, vol. 16, n° 7, p. 4641–4654. doi: 10.1021/acs.jctc.0c00075
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Article (Published version)
Identifiers
ISSN of the journal1549-9618
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49downloads

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